Pages that link to "Item:Q5254949"
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The following pages link to High-Dimensional Variable Selection for Survival Data (Q5254949):
Displaying 31 items.
- The benefit of data-based model complexity selection via prediction error curves in time-to-event data (Q63246) (← links)
- Root- estimability of some missing data models (Q765837) (← links)
- \(L_1\) splitting rules in survival forests (Q1641909) (← links)
- Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting (Q1672811) (← links)
- A review of survival trees (Q1950331) (← links)
- Discrete-time survival forests with Hellinger distance decision trees (Q1987191) (← links)
- Nonparametric feature selection by random forests and deep neural networks (Q2129580) (← links)
- Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors (Q2172011) (← links)
- Random forest with acceptance-rejection trees (Q2203396) (← links)
- The effect of splitting on random forests (Q2347711) (← links)
- A random forest based approach for predicting spreads in the primary catastrophe bond market (Q2665850) (← links)
- Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets (Q2800196) (← links)
- Survival analysis with high-dimensional covariates: an application in microarray studies (Q2864056) (← links)
- A fast adaptive Lasso for the cox regression via safe screening rules (Q3389652) (← links)
- Novel Aggregate Deletion/Substitution/Addition Learning Algorithms for Recursive Partitioning (Q3391141) (← links)
- Recursively Imputed Survival Trees (Q4916465) (← links)
- Random survival forests for high‐dimensional data (Q4969754) (← links)
- Bayesian survival trees for clustered observations, applied to tooth prognosis (Q4969929) (← links)
- Predicting risk for adverse health events using random forest (Q5036398) (← links)
- Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models (Q5057087) (← links)
- <i>L</i><sub>0</sub>-Regularized Learning for High-Dimensional Additive Hazards Regression (Q5058017) (← links)
- High dimensional variable selection with clustered data: an application of random multivariate survival forests for detection of outlier medical device components (Q5107399) (← links)
- Shortcomings of Transfer Entropy and Partial Transfer Entropy: Extending Them to Escape the Curse of Dimensionality (Q5148912) (← links)
- Censoring Unbiased Regression Trees and Ensembles (Q5229919) (← links)
- The Impact of Churn on Client Value in Health Insurance, Evaluation Using a Random Forest Under Various Censoring Mechanisms (Q5881989) (← links)
- Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies (Q6543882) (← links)
- Ranking of average treatment effects with generalized random forests for time-to-event outcomes (Q6617512) (← links)
- A new method for clustered survival data: estimation of treatment effect heterogeneity and variable selection (Q6625363) (← links)
- A review on statistical and machine learning competing risks methods (Q6625438) (← links)
- Transfer learning via random forests: a one-shot federated approach (Q6626701) (← links)
- Exploratory identification of predictive biomarkers in randomized trials with normal endpoints (Q6627504) (← links)
